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- W4214888199 abstract "The purpose of action detection is to detect whether the specified action behavior occurs in the video, and find out the time when the action occurs. Predecessors introduced pose information into the field of action detection through RNN and GCN, but because of the gradient disappearance problem of recurrent neural network (RNN) and the problem of graph convolution (GCN) calculation efficiency, they encountered bottlenecks. In this paper, we propose to use convolutional neural network (CNN) to introduce pose information into behavior detection by means of transfer learning. We use CenterNet to obtain high-semantic information to improve the accuracy of behavior detection. In order to generate a more suitable pose heatmap of the application scenario, our CenterNet pose estimation network participates in the training, and constantly updates the parameters of the pose estimation network according to the new dataset. We conducted a large number of ablation experiments on the Ur fall detection dataset and RWF-2000 dataset. On the RWF-2000 data set, we added a pose estimation network to generate a pose heatmap and input it into the subsequent network, the accuracy increased from 79.0 to 83.75%." @default.
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- W4214888199 date "2022-01-01" @default.
- W4214888199 modified "2023-09-25" @default.
- W4214888199 title "Action Detection Based on Transfer Learning of Human Pose Estimation" @default.
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- W4214888199 doi "https://doi.org/10.1007/978-981-16-6963-7_20" @default.
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